Voxel map generator and method thereof

ABSTRACT

A volume cell (VOXEL) map generation apparatus includes an inertia measurement unit to calculate inertia information by calculating inertia of a volume cell (VOXEL) map generator, a Time of Flight (TOF) camera to capture an image of an object, thereby generating a depth image of the object and a black-and-white image of the object, an estimation unit to calculate position and posture information of the VOXEL map generator by performing an Iterative Closest Point (ICP) algorithm on the basis of the depth image of the object, and to recursively estimate a position and posture of the VOXEL map generator on the basis of VOXEL map generator inertia information calculated by the inertia measurement unit and VOXEL map generator position and posture information calculated by the ICP algorithm, and a grid map construction unit to configure a grid map based on the recursively estimated VOXEL map generator position and posture.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Korean Patent Application No.2010-0098927, filed on Oct. 11, 2010 in the Korean Intellectual PropertyOffice, the disclosure of which is incorporated herein by reference.

BACKGROUND

1. Field

Embodiments of the present disclosure relate to a volume cell (VOXEL)map generator for integrating Simultaneous Localization And Mapping(SLAM) into Iterative Closest Point (ICP) and a method for use in theVOXEL map generator.

2. Description of the Related Art

In recent times, many companies are conducting intensive research into3D scan sensors, each of which generates a disparity map includingdistance information using a Time of Flight (TOF) camera differentlyfrom a conventional camera capable of providing only image information.Such sensors can obtain image brightness information from each pixel,and can also recognize a sensor for each pixel and distance informationdetected from each pixel. Accordingly, such sensors have been widelyutilized in SLAM and obstacle detection. Infrared TOF cameras havenumerous applications. For example, a digital device User Interface (UI)based on motion capture, a security system implementation through userrecognition based on infrared characteristics, a 3D environmentreconstruction serving as a 3D navigation technology, and a matchingtechnology such as ICP have been widely utilized.

Conventionally, the matching technology is carried out through the ICPtechnology so as to perform localization. In contrast, the ICPtechnology again performs the ICP operation under the condition thaterroneous matching is made on errors, such that there is a highpossibility of 3D errors.

SUMMARY

Therefore, it is an aspect of the present disclosure to provide a VOXELmap generator which implements a VOXEL map for writing information aboutthe presence or absence of an object in a virtual grid space, andobviates an error encountered when an ICP algorithm generates a VOXELmap through an inertia measurement apparatus and video-based SLAM.

Additional aspects of the disclosure will be set forth in part in thedescription which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

In accordance with one aspect of the present disclosure, a volume cell(VOXEL) map generation apparatus includes an inertia measurement unit tocalculate inertia information by calculating inertia of a volume cell(VOXEL) map generator, a Time of Flight (TOF) camera to capture an imageof an object, thereby generating a depth image of the object and ablack-and-white image of the object, an estimation unit to calculateposition and posture information of the VOXEL map generator byperforming an Iterative Closest Point (ICP) algorithm on the basis ofthe depth image of the object, and to recursively estimate a positionand posture of the VOXEL map generator on the basis of not only VOXELmap generator inertia information calculated by the inertia measurementunit but also VOXEL map generator position and posture informationcalculated by the ICP algorithm, and a grid map construction unit toconfigure a grid map on the basis of the recursively estimated VOXEL mapgenerator position and posture and information about the object measuredby the TOF camera.

The estimation unit performs simultaneous localization and mapping(SLAM) based on the black-and-white image captured by the TOF camera,such a position and posture of the VOXEL map generator are estimated.

The estimation unit converts a matrix related to the VOXEL map generatorposition and posture information calculated by performing simultaneouslocalization and mapping (SLAM) based on the black-and-white image ofthe object into an error covariance format, such that a position andposture of the VOXEL map generator are estimated.

The estimation unit converts the matrix related to the VOXEL mapgenerator position and posture information calculated by performing aniterative closest point (ICP) algorithm on a depth image of the objectinto an error covariance format, such that a position and posture of theVOXEL map generator are estimated.

The error covariance of the matrix related to the VOXEL map generatorposition and posture information calculated by execution of thesimultaneous localization and mapping (SLAM) based on video iscalculated on the basis of state variables of the object position andposture information matrix calculated by execution of simultaneouslocalization and mapping (SLAM) based on the black-and-white image ofthe object.

The error covariance of the matrix related to the VOXEL map generatorposition and posture information calculated by execution of simultaneouslocalization and mapping (SLAM) based on video is calculated on thebasis of state variables of the object position and posture informationmatrix calculated by execution of simultaneous localization and mapping(SLAM) based on the black-and-white image of the object.

The estimation unit calculates an error covariance of the matrix relatedto the VOXEL map generator position and posture information calculatedby execution of simultaneous localization and mapping (SLAM) based onvideo, calculates the Jacobian of the VOXEL map generator position andposture information matrix calculated by execution of the ICP algorithm,and estimates a position and posture of the VOXEL map generator inresponse to a gain calculated through the error covariance that modifiesa state variable on the basis of the Jacobian result.

In accordance with another aspect of the present disclosure, a methodfor generating a volume cell (VOXEL) includes calculating inertiainformation by measuring inertia of a VOXEL map generator, capturing animage of an object through a Time of Flight (TOF) camera, therebygenerating a depth image of the object, calculating position and postureinformation of the VOXEL map generator by performing an IterativeClosest Point (ICP) algorithm on the depth image of the object,repeatedly estimating a position and posture of the VOXEL map generatoraccording to a measurement angle on the basis of not only the calculatedVOXEL map generator inertia information but also the VOXEL map generatorposition and posture information calculated by execution of theIterative Closest Point (ICP) algorithm, and constructing a grid map onthe basis of the repeatedly estimated VOXEL map generator position andposture information.

In accordance with another aspect of the present disclosure, a methodfor reducing errors of a position and posture of a volume cell (VOXEL)map generator includes capturing an image of an object through a Time ofFlight (TOF) camera, and generating a depth image of the object,calculating position and posture information of a VOXEL map generator byperforming an Iterative Closest Point (ICP) algorithm on a depth imageof the object, and estimating a position and posture of the VOXEL mapgenerator, and converting a matrix related to the calculated VOXEL mapgenerator position and posture information into an error covarianceformat, thereby reducing errors of the estimated VOXEL map generatorposition and posture.

In accordance with another aspect of the present disclosure, a methodfor reducing errors of a position and posture of a volume cell (VOXEL)map generator includes calculating inertia information by measuringinertia of a VOXEL map generator, capturing an image of an objectthrough a Time of Flight (TOF) camera, thereby generating a depth imageof the object, calculating position and posture information of the VOXELmap generator on the basis of not only the calculated VOXEL mapgenerator inertia information but also VOXEL map generator position andposture information calculated by an iterative closest point (ICP)algorithm, and converting a matrix of the calculated VOXEL map generatorinertia information and a matrix of the VOXEL map generator position andposture information calculated using the ICP algorithm into an errorcovariance format, thereby reducing errors of the estimated VOXEL mapgenerator position and posture.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects of the disclosure will become apparent andmore readily appreciated from the following description of theembodiments, taken in conjunction with the accompanying drawings ofwhich:

FIG. 1 is a perspective view illustrating a VOXEL map generatoraccording to an exemplary embodiment of the present disclosure.

FIG. 2 shows a method for implementing a video-based'SLAM technology foruse in a VOXEL map generation method according to another embodiment ofthe present disclosure.

FIG. 3 is a conceptual diagram illustrating a method for generating aVOXEL map according to another embodiment of the present disclosure.

FIGS. 4 and 5 show a VOXEL map obtained through other embodiments of thepresent disclosure.

FIG. 6 shows a humanoid footstep navigation based on a VOXEL mapobtained through other embodiments of the present disclosure.

FIG. 7 is a block diagram illustrating a method for generating a VOXELmap according to another embodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the embodiments of the presentdisclosure, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout.

The 3D occupancy gridmap divides a space into several areas using apredetermined grid, and engraves the probability indicating whether acorresponding space is filled or empty in the corresponding space, suchthat it can obtain information about the space. A unit grid is referredto as a volume cell (VOXEL), and a gridmap formed by such grid is aVOXEL map.

The embodiments of the present disclosure will hereinafter be describedwith reference to the accompanying drawings.

FIG. 1 is a perspective view illustrating a VOXEL map generatoraccording to an exemplary embodiment of the present disclosure.

Referring to FIG. 1, the VOXEL map generator 1 includes an inertiameasurement apparatus 100, a TOF camera 200, an estimator 300, and agrid map generator 400.

The inertia measurement apparatus 100 includes an acceleration sensor130 and a gyro sensor 160. The number of acceleration sensors is 3 andthe number of gyro sensors is 3 such that the inertia measurementapparatus 100 can obtain acceleration and angular velocity informationwith 6 DOF (Degrees of Freedom).

The acceleration sensor 130 measures acceleration of the target object5, and includes an X-axis accelerometer, a Y-axis accelerometer, and aZ-axis accelerometer.

The TOF camera 200 includes a light emitting part 230 and a lightreceiving part 260. If the light emitting part 230 outputs infrared raysvarying sinusoidally, an image cell of the light receiving part 260receives light reflected from the target object 5, such that itcalculates the moving distance of the light. On the basis of thecalculated light moving distance, the TOF camera 200 collects severalthousand image cells or tens of thousands of image cells, andaccumulates the collected image cells, such that it makes one-depthimage.

The estimation unit(300) performs the ICP algorithm on the basis of thedepth image of the object so as to calculate position- andposture-information of the object, and estimates the position andposture of the object several times according to a measurement angle onthe basis of not only the object inertia information calculated by theinertia measurement apparatus 100, but also the object position- andposture-information calculated by the ICP algorithm.

The grid map constructing unit configures a grid map on the basis of theobject position and posture information estimated several times. Thisgrid map means a VOXEL map.

In this case, the ICP algorithm performs 1:1 matching about the closestpoints, searches for a conversion point where the sum of the distancevalues reaches a minimum value, and repeats the corresponding processunder the conversion state. In addition, the ICP algorithm searches forthe most probable position between conversion results, such that it canminimize 3D salt and pepper noise. However, the accumulated error isvery large, such that a distorted image may be the end result.

FIG. 2 shows a method for implementing a video-based SLAM technology foruse in a VOXEL map generation method according to another embodiment ofthe present disclosure.

The left image of FIG. 2 is captured by a TOF camera, and the rightimage of FIG. 2 shows video-based SLAM using the captured image.

SLAM is an algorithm for simultaneous localization and mapping thatmonitors a peripheral area while moving in an arbitrary space so that itcan estimate the map and current position of the corresponding space. Inother words, the SLAM maps environmental data to recognizableinformation, and performs localization on the basis of the mappedresult. In addition, real-time image-based SLAM can obtain a currentposition from a camera at any platforms that desire to recognize thecurrent position. A characteristic point map composed of naturallandmarks scattered throughout the entire space is generated by themovement of camera, and at the same time the 3D localization of thecamera is achieved. In the case of the image-based SLAM, because ofinformation acquisition synchronization of the sensor and the TOF cameraor problems associated with number of characteristic points, a fineposition error occurs. Although the position error unavoidably leaves anunclean track to a VOXEL map, the continuously accumulated error is verysmall.

In addition, the TOF camera has a limited viewing angle. In the case ofusing the inertia measurement apparatus 100, the accuracy of theimage-based SLAM posture estimation process is increased, and thereliability of image characteristic point tracking process is alsoincreased.

Image-based SLAM is performed using an extended Kalman filter, and theSLAM result is divided into an estimation process and an update process.Through the update process, information about the inertia measurementapparatus 100 and the image characteristic point position may be appliedto the Kalman filter. In the case of performing the SLAM by addinginertia information measured by the inertia measurement apparatus 100instead of using only the image, the size of an area, that must be foundin the image during the detection process for searching for acharacteristic point, can be greatly reduced, the reliability of theresult is increased and the number of calculations can be reduced.

In addition, the estimation unit(300) performs image-based SLAM on thebasis of the object's depth image, so that it estimates the position andposture of the object. In order to more accurately estimate the positionand posture of the object, an error covariance of the image-based SLAMresult is calculated to estimate the position and posture of the object.First, in order to recognize the position of the VOXEL map generator 1,the amplitude image generated by the TOF camera 200 is utilized. Theamplitude image is data obtained by measuring brightness data, insteadof distance data generated by the TOF camera 200. If the amplitude imageis obtained as a by-product of the distance data, this amplitude imagehas the same format as that of a general CCD camera. Next, image-basedSLAM is used to form an environment map composed of characteristicpoints different from those of the VOXEL map, and at the same timerecognizes the position of the object 5. The image-based SLAM has thefollowing state variables shown in the following Expression.

$\begin{matrix}{{\overset{.}{x} = \begin{bmatrix}V \\{\overset{.}{y}}_{1} \\{\overset{.}{y}}_{2} \\\vdots\end{bmatrix}},{P = \begin{bmatrix}P_{VV} & P_{{Vy}\; 1} & P_{{Vy}\; 2} & \ldots \\P_{y\; 1V} & P_{{Vy}\; 1} & P_{{Vy}\; 2} & \ldots \\P_{y\; 2\; V} & P_{{Vy}\; 1} & P_{{Vy}\; 2} & \ldots \\\vdots & \vdots & \vdots & \ldots\end{bmatrix}}} & \lbrack{Expression}\rbrack\end{matrix}$

In the image-based SLAM, there are two state variables. A posture statevariable representing the position of a sensor system (including theinertia measurement apparatus and the TOF camera) is represented by thefollowing Equation 1.

$\begin{matrix}{V_{k} = {\begin{bmatrix}q_{k} \\x_{k}\end{bmatrix} = \begin{bmatrix}q_{0} & q_{1} & q_{2} & q_{3} & x & y & z\end{bmatrix}^{T}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

The image-based SLAM searches for a video-based invariablecharacteristic point, and calculates the position of the target object 5and the error covariance. In this case, the calculated image-based SLAMcovariance

is obtained by extracting a first ‘Pvv’ value of the matrix P.

However, when measurement information of the TOF camera 200 andexecution information of the image-based SLAM are obtained in the VOXELmap generation process, synchronization may not occur due to a timedifference. In order to solve this problem, the ICP algorithm may beused. In more detail, when the ICP error covariance

is obtained such that the measurement information of the TOF camera 200and the image-based SLAM execution information are obtained, the ICPerror covariance

can solve the problem of synchronization failure caused by a timedifference.

The estimation unit(300) calculates the position and posture informationof the object by performing the ICP algorithm on the basis of the depthimage of the object, such that it can compensate for a vibration errorencountered when only the image-based SLAM is performed. According tothe ICP framework, a rotation conversion and a translation conversion,that make a minimum sum value of the distances between points (p₁ andp₂) each having an orthogonal vector ‘n’, can be calculated by thefollowing Equation 2.

E=Σ[(R _(p1i) +t−p _(2i))·n _(i)]²  [Equation 2]

In Equation 2, ‘R’ is a rotation conversion matrix, and ‘t’ is atranslation conversion vector. Although the rotation conversion matrixis not identical to the linear conversion, the rotation conversion basedon the ICP algorithm is related to a very small angle such that it canbe converted as shown in the following Equation 3.

$\begin{matrix}{R = \begin{bmatrix}1 & {- {\gamma}} & {\beta} \\{\gamma} & 1 & {- {\alpha}} \\{- {\beta}} & {\alpha} & 1\end{bmatrix}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

If Equation 3 is substituted into Equation 2, the following Equation 4can be obtained.

E=Σ _(i)[(p _(1i) −p _(2i))·n _(i) +t·n _(i) +r·q _(i)]²  [Equation 4]

In Equation 4,

and r=

are defined.

In order to obtain a minimum E value, if each of dα, dβ, dγ, dx, dy, dzis partially differentiated, each of the resultant values must be anextreme value, and each of the resultant values is zero. Therefore, thelinear equation shown in Equation 5 can be obtained.

$\begin{matrix}{{{\text{?}{{{\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}}} \cdot {\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}}}} = {{- \text{?}}{{\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}\begin{matrix}\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?} \\\text{?}\end{matrix}}}}}{\text{?}\text{indicates text missing or illegible when filed}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Equation 5 may have

. In Equation 5, a (6×6) matrix I defined as an uncertain ICP matrix,and a (6×1) matrix is an ICP error covariance

, such that state variables of the object's position and postureinformation matrix calculated by the ICP algorithm are defined. The ICPerror covariance

may be defined as an ellipsoid of the 6 DOF space, such that theresultant ellipsoidal equation may be represented by the followingEquation 6.

Equation 6]

The object's position and posture information matrix calculated by theICP algorithm may be defined in a coordinate system composed of dα, dβ,dγ, dx, dy, dz. The ellipsoid is defined as an uncertainty hyperellipsoid. A principal axis of each ellipsoid is comprised of an Eigenvector of the ICP error covariance

. The length of each principal axis indicates the degree of localizationand uncertainty decided by the ICP algorithm. Preferably, according tothe ICP-based localization result, each axis of the uncertainty hyperellipsoid may have a short length. That is, it is necessary to minimizethe Eigen value of the ICP error covariance

.

In order to allow the estimation unit(300) to estimate the position andposture of the target object 5 through the image-based SLAM and ICPalgorithm, it is necessary to calculate an optimum estimation valueusing the optimum gain (K₁) based on the image-based SLAM and ICPalgorithm, as denoted by the following Equation 7.

Equation 7]

In Equation 7, the optimum gain K₁ may be calculated through the ICPerror covariance

and the SLAM error covariance

. First, state variables

and

are different from each other, such that Jacobian of the value iscalculated and the state variables are modified according to the

value. The Jacobian may be defined as shown in Equation 8.

Equation 8]

In Equation 8, an optimum gain K₁ is decided by the following Equation9.

[Equation 9]

The VOXEL map generator 1 according to an exemplary embodiment of thepresent disclosure can more accurately estimate the position and postureof the target object 5 on the basis of the optimum gain K₁. Equation 10indicates a newly generated VOXEL map according to one embodiment of thepresent disclosure.

q({circumflex over (ν)})  [Equation 10]

The newly obtained VOXEL map generates a small amount of noise caused bydistortion, and does not diverge from an actual state.

FIG. 3 is a conceptual diagram illustrating a method for generating aVOXEL map according to another embodiment of the present disclosure.FIG. 3 shows the result of a VOXEL map filled with the occupancyprobabilities.

FIGS. 4 and 5 show a VOXEL map obtained through other embodiments of thepresent disclosure. In FIGS. 4 and 5, stairs, an inclined plane, andcylindrical objects (e.g., a cylindrical trash can) are shown in realtime in the VOXEL map. This VOXEL map has a small amount of noise and nodivergence. As can be seen from FIGS. 4 and 5, a horizontal plane ofeach stair and an angle of the incline plane may be relatively andclearly shown, and a circular surface of the cylinder is very close toan actual shape thereof.

FIG. 6 shows a humanoid footstep navigation based on a VOXEL mapobtained through other embodiments of the present disclosure.

In FIG. 6, a footstep planner for generating a humanoid's walking trackis considered to be a representative application method that can beobtained through a VOXEL map. The VOXEL map of FIG. 5 shows thepositions of obstacles, and the footstep planner generates an evasivepath.

FIG. 7 is a block diagram illustrating a method for generating a VOXELmap according to another embodiment of the present disclosure.

Referring to FIG. 7, the VOXEL map generator obtains acceleration dataand gyro data through the acceleration sensor 130 and the gyro sensor160 contained in the inertia measurement apparatus 100. The obtainedacceleration data and gyro data are indicative of inertia information ofthe target object 5, and changes inertia information into the positionand posture information of the object using the ICP algorithm. The image(i.e., a depth image) obtained through the TOF camera 200 or 3D data isobtained. Subsequently, the obtained image or 3D data is processedaccording to the image-based SLAM algorithm using the extended Kalmanfilter. The information calculated by the image-based SLAM is combinedwith the inertia information calculated by the ICP algorithm, such thatthe position and posture information of the target object 5 areestimated. In this case, individual covariances of the image-based SLAMprocess and the ICP algorithm are obtained, such that an optimum gain K₁calculated between individual covariance values is extracted. The newposition and posture data are obtained through the extracted optimumgain K₁, are then applied to the extended Kalman filter, such that moreaccurate position and posture are estimated to form the VOXEL map, orthe VOXEL map may be directly generated without any process.

The method for reducing errors of the position and posture of the VOXELmap generator according to still another embodiment of the presentdisclosure captures the object using the TOF camera 200 to generate adepth image of the object, performs the ICP algorithm on the basis ofthe depth image of the object, calculates the position and postureinformation of the VOXEL map generator, and converts a matrix indicatingthe position and posture information of the VOXEL map generator into anerror covariance, such that it can reduce the errors of the position andposture of the VOXEL map generator.

A method for reducing errors of the position and posture of the VOXELmap generator according to yet another embodiment of the presentdisclosure measures inertia of the VOXEL map generator to calculateinertia information, captures the object through the TOF camera 200 togenerate a depth image of the object, estimates the position and postureinformation of the object on the basis of calculated inertia informationand the VOXEL map generator position and posture information calculatedusing the ICP algorithm, and converts a matrix indicating the calculatedVOXEL map generator inertia information and a matrix indicating theICP-processed VOXEL map generator position and posture information intoan error covariance, such that it can reduce the estimated position andposture errors of the VOXEL map generator.

As is apparent from the above description, the VOXEL map generator and amethod for use in the same according to the embodiments of the presentdisclosure can minimize errors encountered when an algorithm is executedby both the ICP inertia measurement apparatus for generating a VOXEL mapand a video-based SLAM technology.

Although a few embodiments of the present disclosure have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from theprinciples and spirit of the invention, the scope of which is defined inthe claims and their equivalents.

1. A volume cell (VOXEL) map generation apparatus comprising: an inertiameasurement unit to calculate inertia information by calculating inertiaof a volume cell (VOXEL) map generator; a Time of Flight (TOF) camera tocapture an image of an object, thereby generating a depth image of theobject and a black-and-white image of the object; an estimation unit tocalculate position and posture information of the VOXEL map generator byexecuting an Iterative Closest Point (ICP) algorithm on the basis of thedepth image of the object, and to recursively estimate a position andposture of the VOXEL map generator on the basis of not only VOXEL mapgenerator inertia information calculated by the inertia measurement unitbut also VOXEL map generator position and posture information calculatedby the ICP algorithm; and a grid map construction unit to configure agrid map on the basis of the recursively estimated VOXEL map generatorposition and posture and information about the object measured by theTOF camera.
 2. The apparatus according to claim 1, wherein theestimation unit performs simultaneous localization and mapping (SLAM)based on the black-and-white image captured by the TOF camera, such thatit estimates a position and posture of the VOXEL map generator.
 3. Theapparatus according to claim 2, wherein the estimation unit converts amatrix related to the VOXEL map generator position and postureinformation calculated by performing the simultaneous localization andmapping (SLAM) based on the black-and-white image of the object into anerror covariance format, such that it estimates a position and postureof the VOXEL map generator.
 4. The apparatus according to claim 3,wherein the estimation unit converts the matrix related to the VOXEL mapgenerator position and posture information calculated by performing aniterative closest point (ICP) algorithm on a depth image of the objectinto an error covariance format, such that a position and posture of theVOXEL map generator are estimated.
 5. The apparatus according to claim4, wherein the error covariance of the matrix related to the VOXEL mapgenerator position and posture information calculated by execution ofthe simultaneous localization and mapping (SLAM) based on video iscalculated on the basis of state variables of the object position andposture information matrix calculated by execution of simultaneouslocalization and mapping (SLAM) based on the black-and-white image ofthe object.
 6. The apparatus according to claim 5, wherein the errorcovariance of the matrix related to the VOXEL map generator position andposture information calculated by execution of the ICP algorithm iscalculated on the basis of state variables of the VOXEL map generatorposition and posture information calculated by execution of the ICPalgorithm based on the depth image of the object.
 7. The apparatusaccording to claim 6, wherein the estimation unit calculates an errorcovariance of the matrix related to the VOXEL map generator position andposture information calculated by execution of the simultaneouslocalization and mapping (SLAM) based on video, calculates Jacobian ofthe VOXEL map generator position and posture information matrixcalculated by execution of the ICP algorithm, and estimates a positionand posture of the VOXEL map generator in response to a gain calculatedthrough the error covariance that modifies a state variable on the basisof the Jacobian result.
 8. The apparatus according to claim 8, whereinthe inertia measurement apparatus includes a gyro sensor or anacceleration sensor.
 9. A method for generating a volume cell (VOXEL)comprising: calculating inertia information by measuring inertia of aVOXEL map generator; capturing an image of an object through a Time ofFlight (TOF) camera, thereby generating a depth image of the object;calculating position and posture information of the VOXEL map generatorby performing an Iterative Closest Point (ICP) algorithm on the depthimage of the object; repeatedly estimating a position and posture of theVOXEL map generator according to a measurement angle on the basis of thecalculated VOXEL map generator inertia information and the VOXEL mapgenerator position and posture information calculated by execution ofthe Iterative Closest Point (ICP) algorithm; and constructing a grid mapon the basis of the repeatedly estimated VOXEL map generator positionand posture information.
 10. The method according to claim 9, furthercomprising performing simultaneous localization and mapping (SLAM) basedon a black-and-white image captured by the TOF camera.
 11. The methodaccording to claim 10, further comprising converting a matrix related tothe VOXEL map generator position and posture information calculated byperforming the simultaneous localization and mapping (SLAM) based on theblack-and-white image of the object into an error covariance format. 12.The method according to claim 11, further comprising converting thematrix related to the VOXEL map generator position and postureinformation calculated by performing an iterative closest point (ICP)algorithm on a depth image of the object into an error covarianceformat.
 13. The method according to claim 12, wherein the errorcovariance of the matrix related to the VOXEL map generator position andposture information calculated by execution of the simultaneouslocalization and mapping (SLAM) based on video is calculated on thebasis of state variables of the object position and posture informationmatrix calculated by execution of simultaneous localization and mapping(SLAM) based on the black-and-white image of the object.
 14. The methodaccording to claim 13, wherein the error covariance of the matrixrelated to the VOXEL map generator position and posture informationcalculated by execution of the ICP algorithm is calculated on the basisof state variables of the VOXEL map generator position and postureinformation calculated by execution of the ICP algorithm based on thedepth image of the object.
 15. The method according to claim 14, furthercomprising: calculating an error covariance of the matrix related to theVOXEL map generator position and posture information calculated byexecution of the simultaneous localization and mapping (SLAM) based onvideo; calculating Jacobian of the VOXEL map generator position andposture information matrix calculated by execution of the ICP algorithm;and estimating a position and posture of the VOXEL map generator inresponse to a gain calculated through the error covariance that modifiesa state variable on the basis of the Jacobian result.
 16. A method forreducing errors of a position and posture of a volume cell (VOXEL) mapgenerator, the method comprising: capturing an image of an objectthrough a Time of Flight (TOF) camera, and generating a depth image ofthe object; calculating position and posture information of a VOXEL mapgenerator by performing an Iterative Closest Point (ICP) algorithm on adepth image of the object, and estimating a position and posture of theVOXEL map generator; and converting a matrix related to the calculatedVOXEL map generator position and posture information into an errorcovariance format, thereby reducing errors of the estimated VOXEL mapgenerator position and posture.
 17. A method for reducing errors of aposition and posture of a volume cell (VOXEL) map generator, the methodcomprising: calculating inertia information by measuring inertia of aVOXEL map generator; capturing an image of an object through a Time ofFlight (TOF) camera, thereby generating a depth image of the object;calculating position and posture information of the VOXEL map generatoron the basis of not only the calculated VOXEL map generator inertiainformation but also VOXEL map generator position and postureinformation calculated by an iterative closest point (ICP) algorithm;and converting a matrix of the calculated VOXEL map generator inertiainformation and a matrix of the VOXEL map generator position and postureinformation calculated by execution of the ICP algorithm into an errorcovariance format, thereby reducing errors of the estimated VOXEL mapgenerator position and posture.